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---
title: "Microbiome data science with R/Bioconductor"
site: bookdown::bookdown_site
documentclass: book
bibliography: [packages.bib]
biblio-style: apalike
link-citations: yes
github-repo: microbiome/miacourse
description: "Course material"
output:
bookdown::gitbook
bookdown::pdf_document2
always_allow_html: true
classoption: oneside
geometry:
- top=30mm
- left=15mm
---
# Overview
<a href="https://bioconductor.org"><img src="`r rebook::BiocSticker('animated')`" width="200" alt="Bioconductor Sticker" align="right" style="margin: 0 1em 0 1em" /></a>
## Schedule
Download the [full schedule](SPARCworkshop2023schedule.pdf).
The schedule is summarized as follows.
- Day 1 (Tue) - Symposium; online lectures and no hands-on session
- Day 2 (Wed) - Online lectures; hands-on session on **R/Bioconductor framework**
- Day 3 (Thu) - Online lectures; hands-on session on **microbiome data analysis methods**
- Day 4 (Fri) - Online lectures; advanced microbiome **data analysis methods**
## Learning goals
This course will teach the **basics of microbiome data analysis and
integration with R/Bioconductor**, a popular open source environment
for scientific data analysis.
You will get an overview of the reproducible data analysis workflow,
with recent examples from published studies.
After the course you will know how to approach new tasks in microbiome
data science by utilizing the available R tools and documentation. In
particular, you understand the concepts of data containers,
reproducible workflows, and standard concepts in microbiome data
analysis.
## Target audience
The course is primarily designed for advanced MSc and PhD students,
Postdocs, and biomedical researchers who wish to learn and develop new
skills in scientific programming and microbiome data science.
Academic students and researchers from Finland and abroad are welcome
and encouraged to apply. The course has limited capacity, and priority
will given for local students.
**Expected background** Earlier experience with R or another
programming language is expected. The teaching format allows
adaptations according to the student's learning speed.
## Learning material
The teaching builds on the open online tutorial, Orchestrating
Microbiome Analysis (https://microbiome.github.io/OMA). The openly
licensed teaching material will be available online during and after
the course, following [recommendations on open education](https://edition.fi/tsv/catalog/book/421).
The training material walks you through the standard steps of
microbiome data analysis covering data import, processing,
exploration, analysis, visualization, reproducible reporting, and best
practices in open science. We teach generic data analytical skills
that are applicable to common data analysis tasks encountered in
modern omics research. The teaching format allows adaptations
according to the student's learning speed.
Link to online Gitter chat:
[https://microbiome.github.io](https://microbiome.github.io)
# Checklist: preparing for the course
## Questionnaire on the background of participants
Fill in the anonymous [questionnaire](https://forms.gle/XZdiEyGyYtLKYwqp8).
This information will help us to understand the background of the
participants better, and adjust teaching accordingly.
## Installing the required R/Bioconductor packages {#packages}
Install the required software in advance.
* [R (it is critical to use the latest official release!)](https://www.r-project.org/)
* [RStudio](https://www.rstudio.com/products/rstudio/download/);
choose "Rstudio Desktop" to download the latest version. For further
details, check the [Rstudio home page](https://www.rstudio.com/).
* Install and load the required R packages (see below)
* After a successful installation you can start with the
case study examples in the training material
### Required R/Bioconductor packages
This section shows how to install and load all required packages into
the R session, if needed. Only uninstalled packages are installed.
```{r warning = FALSE, message = FALSE, eval=FALSE}
# List of packages that we need from cran and bioc
cran_pkg <- c("BiocManager", "bookdown", "dplyr", "ecodist", "ggplot2",
"gridExtra", "kableExtra", "knitr", "scales", "vegan", "matrixStats")
bioc_pkg <- c("yulab.utils","ggtree","ANCOMBC", "ape", "DESeq2", "DirichletMultinomial", "mia", "miaViz", "miaSim")
github_pkg <- c("miaTime")
# Get those packages that are already installed
cran_pkg_already_installed <- cran_pkg[ cran_pkg %in% installed.packages() ]
bioc_pkg_already_installed <- bioc_pkg[ bioc_pkg %in% installed.packages() ]
github_pkg_already_installed <- github_pkg[ github_pkg %in% installed.packages() ]
# Get those packages that need to be installed
cran_pkg_to_be_installed <- setdiff(cran_pkg, cran_pkg_already_installed)
bioc_pkg_to_be_installed <- setdiff(bioc_pkg, bioc_pkg_already_installed)
github_pkg_to_be_installed <- setdiff(github_pkg, github_pkg_already_installed)
# Reorders bioc packages, so that mia and miaViz are first
bioc_pkg <- c(bioc_pkg[ bioc_pkg %in% c("mia", "miaViz") ],
bioc_pkg[ !bioc_pkg %in% c("mia", "miaViz") ] )
# Combine to one vector
packages <- c(bioc_pkg, cran_pkg)
packages_to_install <- c( bioc_pkg_to_be_installed, cran_pkg_to_be_installed, cran_pkg_to_be_installed)
```
```{r warning = FALSE, message = FALSE, eval=FALSE}
# If there are packages that need to be installed, install them
if( length(packages_to_install) ) {
BiocManager::install(packages_to_install)
}
```
Now all required packages are installed, so let's load them into the session.
Some function names occur in multiple packages. That is why miaverse's packages
mia and miaViz are prioritized. Packages that are loaded first have higher priority.
```{r warning = FALSE, message = FALSE, results="hide", eval=FALSE}
# Loading all packages into session. Returns true if package was successfully loaded.
loaded <- sapply(packages, require, character.only = TRUE)
as.data.frame(loaded)
```
## Reading and support
* View the short online videos on [R/Bioconductor microbiome data science tools](https://www.youtube.com/playlist?list=PLjiXAZO27elAJEptP59BN3whVJ61XIkST).
* Check the Appendix chapter of the [OMA
book](https://microbiome.github.io/OMA). In particular, read Chapter
15.3 on reproducible reporting.
* **You can run the workflows by simply copy-pasting the examples.** For
further, advanced material, you can test and modify further examples
from the book, and apply these techniques to your own data.
* Online support on installation and other matters, join us at [Gitter](https://gitter.im/microbiome/miaverse?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
# Acknowledgments
## Teachers and organizers
- [Leo Lahti](https://datascience.utu.fi) is the main teacher and
Associate Professor in Data Science at the University of Turku,
Finland, with specialization on microbiome research.
- Prof. Richa Ashma; local organizer.
- Doctoral candidate Renuka Potbhare; course assistant
## Support
The course is funded by SPARC, and jointly organized by:
- Savitribai Phule Pune University, Pune, India
- Department of Computing, University of Turku, Finland
- CompLifeSci Biocity Research Program, Turku, Finland
The teaching materials have been developed with support from
- ML4microbiome COST action
- Horizon/RIA project FindingPheno
- CompLifeSci Biocity Research Program, Turku, Finland
- Turku University Foundation
- Academy of Finland
**Citation** We thank all [developers and contributors](https://microbiome.github.io) who have contributed open resources that supported the development of the training material. Kindly cite the course material as @miacourse
**License and source code**
All material is released under the open [CC BY-NC-SA 3.0
License](LICENSE) and available online during and after the course,
following the [recommendations on open teaching
materials](https://avointiede.fi/fi/linjaukset-ja-aineistot/kotimaiset-linjaukset/oppimisen-ja-oppimateriaalien-avoimuuden-linjaus)
of the national open science coordination in Finland**.